Next generation high throughput lipidomics using adaptive modelling. This project aims to develop a unique high-throughput method to capture the lipidomic profile of human plasma suitable for large human population screening. Lipids are fundamental to every biological system, but our understanding of their regulation in humans have been largely superficial. By incorporating a new lipidomics approach, with genomic data, this project aims to expand our understanding of human biology by identifying ....Next generation high throughput lipidomics using adaptive modelling. This project aims to develop a unique high-throughput method to capture the lipidomic profile of human plasma suitable for large human population screening. Lipids are fundamental to every biological system, but our understanding of their regulation in humans have been largely superficial. By incorporating a new lipidomics approach, with genomic data, this project aims to expand our understanding of human biology by identifying regulators of lipid metabolism. The large diversity in humans necessitate sufficient sample sizes to identify true genetic regulators, but to date techniques capturing phenotypic data (lipids) have been largely limited. It is anticipated that this study will identify new regulators of lipid metabolism in humans.Read moreRead less
Empirical and computational solutions for multi-omics single-cell assays. Emerging single-cell sequencing technologies are transforming molecular cell biology, but identifying novel cell types and their functions requires the integration of highly heterogeneous data. The development of computational methods able to extract biologically relevant results is hindered by the lack of high-quality datasets. This project aims to develop novel sequencing methodologies and generate data to drive our dime ....Empirical and computational solutions for multi-omics single-cell assays. Emerging single-cell sequencing technologies are transforming molecular cell biology, but identifying novel cell types and their functions requires the integration of highly heterogeneous data. The development of computational methods able to extract biologically relevant results is hindered by the lack of high-quality datasets. This project aims to develop novel sequencing methodologies and generate data to drive our dimension reduction multivariate method developments for data integration. By combining in silico and in vivo approaches, the project is anticipated to benefit scientists willing to work in cutting-edge single-cell research by providing useful protocols and tools to generate novel insights in cell biology. Read moreRead less
Searching for near-exact protein models. This project aims to develop novel and efficient heuristic-based algorithms leading to near accurate protein tertiary structure models. Knowledge about protein structures is fundamental to our understanding of living systems. The progress on experimental determination of these structures has been extremely limited and remains an open challenge in molecular biology. Computational prediction of protein structures from sequences is emerging as a promising ap ....Searching for near-exact protein models. This project aims to develop novel and efficient heuristic-based algorithms leading to near accurate protein tertiary structure models. Knowledge about protein structures is fundamental to our understanding of living systems. The progress on experimental determination of these structures has been extremely limited and remains an open challenge in molecular biology. Computational prediction of protein structures from sequences is emerging as a promising approach, but its accuracy is far from satisfactory. The software systems developed in this project will be used in structural identification of target proteins in drug design. This will make drug design process more efficient, saving time and cost, potentially saving lives.Read moreRead less
Identification of causal variants for complex traits. The aim of this project is to identify causal variants for complex traits in cattle and humans. Although most important traits in agriculture, medicine and evolution are complex traits, very few of the genetic variants affecting these traits are known and this undermines our understanding of how genetic variants affect a trait and practical uses of this knowledge. Huge datasets of individuals with genome sequence and phenotypes and new statis ....Identification of causal variants for complex traits. The aim of this project is to identify causal variants for complex traits in cattle and humans. Although most important traits in agriculture, medicine and evolution are complex traits, very few of the genetic variants affecting these traits are known and this undermines our understanding of how genetic variants affect a trait and practical uses of this knowledge. Huge datasets of individuals with genome sequence and phenotypes and new statistical methods provide the opportunity to close this gap. The outcome will be identification of many genomic variants causing variation in complex traits. This will benefit scientific understanding of complex traits and the ability to predict traits for individuals from their genome sequence.Read moreRead less
Real-time phylogenetics for food-borne outbreak surveillance. The project aims to introduce, for the first time, real-time evolutionary analysis of agricultural pathogens so that outbreaks affecting crops and the food supply can be managed precisely and rapidly. An expert team will implement a large-scale data analytics framework in user-friendly software that integrates Australian infectious disease genomics data with global data. Underpinning this work are new theory and algorithms that apply ....Real-time phylogenetics for food-borne outbreak surveillance. The project aims to introduce, for the first time, real-time evolutionary analysis of agricultural pathogens so that outbreaks affecting crops and the food supply can be managed precisely and rapidly. An expert team will implement a large-scale data analytics framework in user-friendly software that integrates Australian infectious disease genomics data with global data. Underpinning this work are new theory and algorithms that apply Sequential Monte Carlo to update phylogenetic analyses continuously as new data arrives. Expected outcomes include new knowledge of statistical algorithms for evolutionary analysis, relevant to biological disciplines beyond infectious disease; and enhanced capacity for infectious disease analysis. Read moreRead less